Probabilistic modeling is a useful tool to analyze and understand real-world data. Central to the success of Bayesian modeling is posterior inference, for which approximate inference algorithms are typically needed in most problems of interest. The two pillars of approximate Bayesian inference are variational and Monte Carlo methods. In the recent years, there have been numerous advances in both methods, which have enabled Bayesian inference in increasingly challenging scenarios involving complex probabilistic models and large datasets.

In this symposium, besides recent advances in approximate inference, we will discuss the impact of Bayesian inference, connecting approximate inference methods with other fields. In particular, we encourage submissions that relate Bayesian inference to the fields of reinforcement learning, causal inference, decision processes, Bayesian compression, or differential privacy, among others. We also encourage submissions that contribute to connecting different approximate inference methods, such as variational inference and Monte Carlo.

Registration

Registration is free but will be limited. Click here to register! More slots may become available as we free up the reserved slots for authors of the accepted papers. If you are unable to register, feel free to sign up on the waiting list. We will contact you if more slots become available. Given the limited seats, please cancel your registration if you know you will not be able to attend.